Update README.md

README.md

@@ -54,7 +54,6 @@ from transformers import (
     AutoModelForSeq2SeqLM,
     AutoTokenizer,
 )
-import numpy as np
 
 
 class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
@@ -71,22 +70,24 @@ class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
         results = super().postprocess(
             model_outputs=model_outputs
         )
-        return [[keyphrase.strip() for keyphrase in result.get("generated_text").split(self.keyphrase_sep_token)] for result in results]
+        return [[keyphrase.strip() for keyphrase in result.get("generated_text").split(self.keyphrase_sep_token) if keyphrase != ""] for result in results]
+
 ```
 
 ```python
 # Load pipeline
-model_name = "DeDeckerThomas/keyphrase-generation-t5-small-openkp"
+model_name = "ml6team/keyphrase-generation-t5-small-openkp"
 generator = KeyphraseGenerationPipeline(model=model_name)
 
 ```python
 text = """
 Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. 
 Since this is a time-consuming process, Artificial Intelligence is used to automate it. 
-Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. 
-The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. 
-Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. 
-These new methods also focus on the semantics and context of a document, which is quite an improvement.
+Currently, classical machine learning methods, that use statistics and linguistics, 
+are widely used for the extraction process. The fact that these methods have been widely used in the community 
+has the advantage that there are many easy-to-use libraries. Now with the recent innovations in NLP, 
+transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics 
+and context of a document, which is quite an improvement.
 """.replace(
     "\n", ""
 )
@@ -99,7 +100,7 @@ print(keyphrases)
 
 ```
 # Output
-[['keyphrase extraction', 'text analysis', 'artificial intelligence', 'classical machine learning', 'statistics']]
+[['keyphrase extraction', 'text analysis', 'artificial intelligence']]
 ```
 
 ## 📚 Training Dataset
@@ -200,4 +201,4 @@ Abstractive keyphrases
 For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
 
 ## 🚨 Issues
-Please feel free to contact Thomas De Decker for any problems with this model.
+Please feel free to start discussions in the Community Tab.